Signal-to-noise ratio determining method and device, and channel equalization method and device

ABSTRACT

There are provided a signal-to-noise ratio determining method and device for a receiving end of an information transmission system, and a channel equalization method and device based on a minimum mean square error (MMSE) equalizer. The signal-to-noise ratio determining method and device are based on an information transmission system in which timing synchronization is achieved by using a structure of a repetitive training sequence. The signal-to-noise ratio determining method includes: acquiring a peak and a valley of an autocorrelation function, where the peak represents a sum of a signal average power and a noise average power, and the valley represents the noise average power; and determining a signal-to-noise ratio based on the peak and the valley.

The present application claims priority to Chinese Patent ApplicationNo. 201810670194.4, titled “SIGNAL-TO-NOISE RATIO DETERMINING METHOD ANDDEVICE, AND CHANNEL EQUALIZATION METHOD AND DEVICE”, filed on Jun. 26,2018 with the Chinese Patent Office, which is incorporated herein byreference in its entirety.

FIELD

The present disclosure relates to the technical field of mobilecommunication systems, and in particular to a signal-to-noise ratiodetermining method and device for a receiving end of an informationtransmission system, and a channel equalization method and device basedon an MMSE equalizer.

BACKGROUND

For an information transmission system with a high transmission rate, amulti-carrier transmission technology or a single-carrier transmissiontechnology may be adopted for transmission. The orthogonal frequencydivision multiplexing (OFDM) technology is a representativemulti-carrier transmission technology. The single carrier frequencydomain equalization (SCFDE) technology is a representativesingle-carrier transmission technology. The following description isgiven by taking an SCFDE system as an example. Reference is made to FIG.1, which is a schematic diagram showing a transmission process of anSCFDE system in the conventional technology. The transmission process isdescribed below. At the transmitting end, channel coding is performed ona binary bit stream, then constellation mapping is performed, and aguard interval (GI) is inserted in the signal, where a cyclic prefix(CP) or a unique word (UW) is generally used as a GI in the SCFDEsystem. Then shaping filtering, up-conversion (DUC), anddigital-to-analog conversion (D/A) are performed on the signal, and thenthe processed signal enters a channel. At the receiving end, an inverseprocess of the transmitting-end process is performed. Theanalog-to-digital conversion (A/D), down-conversion (DDC), matchedfiltering are performed on the signal, and then timing synchronizationand frequency synchronization are performed on the system. Then theguard interval is removed. In this case, the signal is divided into twoparts including a pilot part and a data part. The pilot part is mainlyused for channel estimation. The data part is converted to the frequencydomain through the FFT transform, and then the converted data togetherwith a channel response and a signal-to-noise ratio obtained by thepilot part is used for MMSE frequency domain equalization. The FFTtransform is performed to convert the signal to the time domain. Thendecision and decoding are performed on the converted signal to obtainthe original binary bit stream.

In the SCFDE system, a minimum mean square error (MMSE) equalizationmethod is used for the channel equalization, in which an MMSE equalizeris adopted. In this case, the effects of both a noise and a channel aretaken into consideration, so that the effect of the noise on the systemdoes not increase in a case that there is deep fading on thetransmission channel. The basic operation principle of the MMSEequalizer is described below. An equalizer coefficient is calculated tominimize a mean square of a difference between an equalizer output andan expected signal, which requires to accurately estimate thesignal-to-noise ratio. There are two common signal-to-noise ratioestimation methods, i.e., a data-aided estimation method and a blindestimation method. The data-aided estimation method is adopted by mostexisting systems. Most typically, the Boumard algorithm is generallyadopted, in which the noise power is estimated by using two preamblesymbols. The calculation complexity of this method is large. Further, itis required to further estimate the signal-to-noise ratio based on theestimated noise power.

Therefore, a technical problem to be solved by those skilled in the artis to reduce the calculation amount for the signal-to-noise ratio, so asto stably and reliably estimate the signal-to-noise ratio.

SUMMARY

An object of the present disclosure is to provide a signal-to-noiseratio determining method and device for a receiving end of aninformation transmission system, and a channel equalization method anddevice based on an MMSE equalizer, to reduce the calculation amount fora signal-to-noise ratio, so as to stably and reliably estimate thesignal-to-noise ratio.

In order to achieve the above object, the following technical solutionsare provided in the present disclosure.

There is provided a signal-to-noise ratio determining method for areceiving end of an information transmission system. The signal-to-noiseratio determining method is based on an information transmission systemin which timing synchronization is achieved by using a structure of arepetitive training sequence. The signal-to-noise ratio determiningmethod includes:

acquiring a peak and a valley of an autocorrelation function, where thepeak represents a sum of a signal average power and a noise averagepower, and the valley represents the noise average power; and

determining a signal-to-noise ratio based on the peak and the valley.

The acquiring a peak and a valley of an autocorrelation functionincludes:

determining an autocorrelation function R_(auto)(k+N) which is expressedas

${{R_{auto}\left( {k + N} \right)} = {\frac{1}{N}{\sum_{m = 0}^{N - 1}{{r\left( {k + m} \right)}{r\left( {k + m + N} \right)}^{*}}}}},$

where k represents a subscript related to time, N represents a length ofthe repetitive training sequence, r(k+m) represents a signal at a timeinstant delayed than a time instant k by m sampling periods, mrepresents the number of delayed sampling periods, and (.)* represents aconjugate operation;

determining a peak R_(auto) ^(Δ)(N) of the autocorrelation function fromthe autocorrelation function R_(auto)(k+N), where in a case that thereis no frequency offset in the information transmission system, the peakR_(auto) ^(Δ)(N) is determined as

$\begin{matrix}{{R_{auto}^{\Delta}(N)} = {\frac{1}{N}{\sum_{m = 0}^{N - 1}\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\{\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{{= {P_{signal} + P_{noise}}},}\end{matrix}$

and in a case that there is a frequency offset which is expressed asε=f_(offset)/Δf in the information transmission system, the peakR_(auto) ^(Δ)(N) is determined as

$\begin{matrix}{{R_{auto}^{\Delta}(N)} = {\frac{1}{N}{\sum_{m = 0}^{N - 1}\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\{\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{{= {{P_{signal}e^{j\; 2\; \pi \; k\; {ɛ/N}}} + P_{noise}}},}\end{matrix}$

where f_(offset) represents a carrier offset, and Δf represents asubcarrier frequency interval; and

determining a valley R_(auto) ^(∇)(N) of the autocorrelation functionfrom the autocorrelation function R_(auto)(k+N), where the valleyR_(auto) ^(∇)(N) is determined as

$\begin{matrix}{{R_{auto}^{\nabla}(N)} = {\frac{1}{N}{\sum_{m = 0}^{N - 1}{{w\left( {k_{\nabla} + m} \right)}\left\lbrack {{s_{preamble}\left( {k_{\nabla} + m} \right)} + {w\left( {k_{\nabla} + m + N} \right)}} \right\rbrack}^{*}}}} \\{{= P_{noise}},}\end{matrix}$

where s_(preamble)(k) represents a training sequence, w(k) represents anoise, P_(signal) represents a signal average power, P_(noise)represents a noise average power, k_(Δ) represents a time subscriptcorresponding to the peak, and k_(∇) represents a time subscriptcorresponding to the valley.

The determining a signal-to-noise ratio based on the peak and the valleyincludes:

determining a signal-to-noise ratio SNR based on the peak R_(auto)^(Δ)(N) and the valley R_(auto) ^(∇)(N) according to a signal-to-noiseratio determination rule which is expressed as

${{SNR} = \frac{{{R_{auto}^{\Delta}(N)}} - {{R_{auto}^{\nabla}(N)}}}{{R_{auto}^{\nabla}(N)}}},$

where |⋅| represents an absolute value operation.

A channel equalization method based on an MMSE equalizer is provided.The channel equalization method includes:

acquiring the signal-to-noise ratio determined by performing thesignal-to-noise ratio determining method according to any one of claims1 to 3, and acquiring a frequency domain channel impulse response;

determining an MMSE equalizer coefficient based on the signal-to-noiseratio and the frequency domain channel impulse response;

determining a scale correction factor based on an average frequencydomain channel response, a signal average power and a noise averagepower; and

performing an equalizing process on a received frequency domain signalbased on the MMSE equalizer coefficient and the scale correction factor,to obtain a scale-corrected frequency domain signal.

The determining a scale correction factor based on an average frequencydomain channel response, a signal average power and a noise averagepower includes:

determining a scale correction factor Θ based on an average frequencydomain channel response H, a signal average power P_(signal), and anoise average power P_(noise) according to a scale correction factordetermination rule which is expressed as

${\Theta = \frac{{{\overset{\_}{H}}^{2}P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}P_{signal}}}.$

A signal-to-noise ratio determining device for a receiving end of aninformation transmission system is provided. The signal-to-noise ratiodetermining device is based on an information transmission system inwhich timing synchronization is achieved by using a structure of arepetitive training sequence. The signal-to-noise ratio determiningdevice includes:

a first acquiring module configured to acquire a peak and a valley of anautocorrelation function, where the peak represents a sum of a signalaverage power and a noise average power, and the valley represents thenoise average power; and

a signal-to-noise ratio determining module configured to determine asignal-to-noise ratio based on the peak and the valley.

The first acquiring module includes:

an autocorrelation function determining unit configured to determine anautocorrelation function R_(auto)(k+N) which is expressed as

${{R_{auto}\left( {k + N} \right)} = {\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{r\left( {k + m} \right)}{r\left( {k + m + N} \right)}^{*}}}}},$

where k represents a subscript related to time, N represents a length ofthe repetitive training sequence, r(k+m) represents a signal at a timeinstant delayed than a time instant k by m sampling periods, mrepresents the number of delayed sampling periods, and (.)* represents aconjugate operation;

a first peak determining unit configured to determine a peak R_(auto)^(Δ)(N) of the autocorrelation function from the autocorrelationfunction R_(auto)(k+N), where in a case that there is no frequencyoffset in the information transmission system, the peak R_(auto) ^(Δ)(N)is determined as

$\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{P_{signal} + P_{noise}}}\end{matrix};$

a second peak determining unit configured to determine the peak R_(auto)^(Δ)(N) of the autocorrelation function from the autocorrelationfunction R_(auto)(k+N), where in a case that there is a frequency offsetwhich is expressed as ε=f_(offset)/Δf in the information transmissionsystem, the peak R_(auto) ^(Δ)(N) is determined as

$\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{{P_{signal}e^{j\; 2\pi \; k\; ɛ\text{/}N}} + P_{noise}}}\end{matrix},$

where f_(offset) represents a carrier offset, and Δf represents asubcarrier frequency interval; and

a valley determining unit configured to determine a valley R_(auto)^(∇)(N) of the autocorrelation function from the autocorrelationfunction R_(auto)(k+N), where the valley R_(auto) ^(∇)(N) is determinedas

$\begin{matrix}{{R_{auto}^{\nabla}(N)} = {\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{w\left( {k_{\nabla} + m} \right)}\left\lbrack {{s_{preamble}\left( {k_{\nabla} + m} \right)} + {w\left( {k_{\nabla} + m + N} \right)}} \right\rbrack}^{*}}}} \\{= P_{noise}}\end{matrix},$

where s_(preamble)(k) represents a training sequence, w(k) represents anoise, P_(signal) represents a signal average power, P_(noise)represents a noise average power, k_(Δ) represents a time subscriptcorresponding to the peak, and k_(∇) represents a time subscriptcorresponding to the valley.

The signal-to-noise ratio determining module is configured to determinea signal-to-noise ratio SNR based on the peak R_(auto) ^(Δ)(N) and thevalley R_(auto) ^(∇)(N) according to a signal-to-noise ratiodetermination rule which is expressed as

${{SNR} = \frac{{{R_{auto}^{\Delta}(N)}} - {{R_{auto}^{\nabla}(N)}}}{{R_{auto}^{\nabla}(N)}}},$

where |⋅| represents an absolute value operation.

A channel equalization device based on an MMSE equalizer is provided.The channel equalization device includes:

a second acquiring module configured to: acquire the signal-to-noiseratio determined by the signal-to-noise ratio determining devicedescribed above, and acquire a frequency domain channel impulseresponse;

an equalizer coefficient determining module configured to determine anMMSE equalizer coefficient based on the signal-to-noise ratio and thefrequency domain channel impulse response;

a scale correction factor determining module configured to determine ascale correction factor based on an average frequency domain channelresponse, a signal average power and a noise average power; and

a signal equalization module configured to: perform an equalizingprocess on a received frequency domain signal based on the MMSEequalizer coefficient and the scale correction factor, to obtain ascale-corrected frequency domain signal.

The scale correction factor determining module is configured todetermine a scale correction factor Θ based on an average frequencydomain channel response H, a signal average power P_(signal), and anoise average power P_(noise) according to a scale correction factordetermination rule which is expressed as

$\Theta = {\frac{{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}.}$

It can be seen from above that, in the information transmission systemon which the above solutions are based, time and frequencysynchronization is achieved by using a structure of a repetitivetraining sequence. The maximum value of an autocorrelation function isrequired to be determined, and symbol timing synchronization and carrierfrequency offset estimation are performed respectively based on theposition and the phase of the maximum value. Therefore, in thesignal-to-noise ratio determining method and device for a receiving endof an information transmission system, based on independence between asignal and a noise, a signal-to-noise ratio can be stably and reliablyestimated based on a peak and a valley of the autocorrelation functionwith no additional calculation complexity, so that the calculationamount for the signal-to-noise ratio can be reduced.

Further, in order to solve the problem that the scale of a constellationmap of an output signal of the MMSE equalizer changes with factors suchas the signal-to-noise ratio, a channel equalization method based on anMMSE equalizer is provided. In this method, an MMSE equalizercoefficient is determined based on a stable and reliable signal-to-noiseratio obtained by performing the signal-to-noise ratio determiningmethod described above, and a scale correction factor is determinedbased on an average frequency domain channel response, a signal averagepower and a noise average power. In this way, the MMSE equalizer canhave an excellent equalization performance, and also can stabilize thescale of the constellation map of the output signal of the MMSEequalizer by scale correction, so that a subsequent soft demappingmodule can work normally.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate technical solutions in embodimentsof the present disclosure or in the conventional technology, thedrawings to be used in the description of the embodiments or theconventional technology are briefly described below. Apparently, thedrawings in the following description only show some embodiments of thepresent disclosure, and other drawings may be obtained by those skilledin the art from the drawings without any creative work.

FIG. 1 is a schematic diagram showing a transmission process of an SCFDEsystem in the conventional technology;

FIG. 2 is a schematic flowchart showing a signal-to-noise ratiodetermining method for a receiving end of an information transmissionsystem according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram showing signal-to-noise ratio estimationbased on a structure of a repetitive training sequence according to anembodiment of the present disclosure;

FIG. 4 is a schematic diagram showing a relationship between an actualvalue and an estimated value of an SNR according to an embodiment of thepresent disclosure;

FIG. 5a shows a constellation map of an output signal of an MMSEequalizer in a case that the signal-to-noise ratio is 4 dB according toan embodiment of the present disclosure;

FIG. 5b shows a constellation map of the output signal of the MMSEequalizer in a case that the signal-to-noise ratio is 12 dB according tothe embodiment of the present disclosure;

FIG. 6 is a schematic flowchart showing a channel equalization methodbased on an MMSE equalizer according to an embodiment of the presentdisclosure;

FIG. 7 is a schematic block diagram showing an MMSE channel equalizationmethod according to an embodiment of the present disclosure;

FIG. 8a shows a scale-changed constellation map in a case that thesignal-to-noise ratio is 4 dB according to an embodiment of the presentdisclosure;

FIG. 8b shows an expected constellation map in the case that thesignal-to-noise ratio is 4 dB according to the embodiment of the presentdisclosure;

FIG. 9 is a schematic structural diagram showing a signal-to-noise ratiodetermining device for a receiving end of an information transmissionsystem according to an embodiment of the present disclosure; and

FIG. 10 is a schematic structural diagram showing a channel equalizationdevice based on an MMSE equalizer according to an embodiment of thepresent disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Technical solutions of embodiments of the present disclosure are clearlyand completely described in the following in conjunction with thedrawings of the embodiments of the present disclosure. Apparently, theembodiments described in the following are only some embodiments of thepresent disclosure, rather than all the embodiments. Any otherembodiments obtained by those skilled in the art based on theembodiments in the present disclosure without any creative work fall inthe scope of protection of the present disclosure.

There are provided a signal-to-noise ratio determining method and devicefor a receiving end of an information transmission system, and a channelequalization method and device based on an MMSE equalizer in the presentdisclosure, to reduce the calculation amount for a signal-to-noiseratio, so as to stably and reliably estimate the signal-to-noise ratio.

It is assumed that, a transmitted signal is indicated by s(k), averagepower of the transmitted signal is equal to 1, a sampling frequency isindicated by 1/T, and a channel impulse response of s(k) is indicated byh(l), where l=0,1, . . . ,L−1, and L represents the number of taps ofthe channel impulse response. An additive white gaussian noise may beindicated by w(k), and average power of the noise is indicated byP_(noise). In this case, a received signal r(k) at a receiving end in atime domain may be expressed as:

$\begin{matrix}\begin{matrix}{{r(k)} = {{\sum\limits_{l = 0}^{L - 1}\; {{h(l)}{s\left( {k - l} \right)}}} + {w(k)}}} \\{= {{{h(k)}*{s(k)}} + {w(k)}}}\end{matrix} & (1)\end{matrix}$

where * represents a linear convolution operation. Due to introductionof a cyclic prefix, the linear convolution operation between the signals(k) and the channel impulse response h(k) may be converted into acircular convolution operation. That is, in a case that a duration T_(G)of the cyclic prefix meets T_(G)≥τ_(max), where τ_(max) represents themaximum delay spread, and receiving and transmitting of the system arestrictly synchronized with each other, the following expression may beobtained after the cyclic prefix is removed:

r(k)=h(k)⊗s(k)+w(k), 0≤k≤N _(F)−1  (2)

where └ represents a circular convolution operation, N_(F) representsthe number of FFT points. After an FFT transformation is performed onthe expression (2), a frequency domain expression of the received signalr(k) is obtained as follows.

R(k)=H(k)S(k)+W(k), 0≤k≤N _(F)−1  (3)

where R(k), H(k), S(k), and W(k) respectively indicate frequency domainrepresentations of r(k), h(k), s(k), and w(k). Channel estimation andfrequency domain equalization for the SCFDE system are performed basedon the expression (3).

It is assumed that an equalizer coefficient is indicated by C(k). Inthis case, an equalized frequency domain output may be expressed as

Ŝ(k)=C(k)H(k)S(k)+C(k)W(k), 0≤k≤N _(F)−1  (4)

According to a definition of a mean square error MSE, the MSE afterequalization is derived as follows.

$\begin{matrix}\begin{matrix}{{MSE} = {E\left\lbrack {\sum\limits_{k = 0}^{N_{F} - 1}\; {{{\hat{S}(k)} - {S(k)}}}^{2}} \right\rbrack}} \\{= {\sum\limits_{k = 0}^{N_{F} - 1}\; {E\left\lbrack {{{\left( {{{C(k)}{H(k)}} - 1} \right){S(k)}} + {{C(k)}{W(k)}}}}^{2} \right\rbrack}}}\end{matrix} & (5)\end{matrix}$

In a case that the mean square error MSE reaches the minimum, a minimummean square error MMSE equalizer having the following equalizercoefficient is obtained.

$\begin{matrix}{{C_{MMSE}(k)} = {\frac{{H^{*}(k)}P_{signal}}{{{{H(k)}}^{2}\mspace{14mu} P_{signal}} + P_{noise}} = \frac{H^{*}(k)}{{{H(k)}}^{2} + \frac{1}{SNR}}}} & (6)\end{matrix}$

where P_(signal) represents a signal average power, P_(noise) representsa noise average power, and SNR represents a signal-to-noise ratio. Itcan be seen from the expression (6) that, for the implementation of theMMSE equalizer, the following two problems are required to be solved.One problem to be solved is that the signal-to-noise ratio should beaccurately estimated, which is crucial in implementing the MMSEequalization. The other one problem to be solved is that, automatic gaincontrol (AGC) of the system cannot ensure that the average power of thereceived signal is fixed at a certain value, but only can ensure thatthe average power of the received signal is in a certain range. In thiscase, relative values of parameters such as the signal average power,the noise average power and the channel estimation value may change,which results in a change of a scale of a constellation map of an outputsignal after equalization, and further results in a soft demappingmodule failing to work normally. Therefore, the scale change is requiredto be suppressed.

In most of existing data-aided signal-to-noise ratio estimation methods,the signal-to-noise ratio is estimated without considering the wholesystem. In this case, a frame structure is always required to bedesigned separately, and calculation complexity is large. Referring toFIG. 2, a signal-to-noise ratio determining method for a receiving endof an information transmission system is provided according to anembodiment of the present disclosure. The signal-to-noise ratiodetermining method is based on an information transmission system inwhich timing synchronization is achieved by using a structure of arepetitive training sequence. The signal-to-noise ratio determiningmethod includes the following steps S101 and S102.

In S101, a peak and a valley of an autocorrelation function areacquired. The peak represents a sum of a signal average power and anoise average power, and the valley represents the noise average power.

It should be noted that, in an information transmission system such asan SCFDE system and an OFDM system, a repetitive pilot structure isusually used to achieve time and frequency synchronization, and relatedparameters of an autocorrelation function are required to be determinedin a process of implementing the time and frequency synchronizationalgorithm by using a structure of a repetitive training sequence.Specifically, symbol timing synchronization and carrier frequency offsetestimation are respectively performed based on a position and a phase ofthe maximum value of the autocorrelation function. Therefore, with thissolution, in the process of implementing the time and frequencysynchronization, the signal-to-noise ratio can be determined based onthe peak and the valley of the autocorrelation function with noadditional calculation complexity, thereby achieving the estimation forthe signal-to-noise ratio and facilitating hardware implementation.

In S102, a signal-to-noise ratio is determined based on the peak and thevalley.

It can be understood that, the signal-to-noise ratio can be determinedbased on the peak and the valley of the autocorrelation function,because the peak of the autocorrelation function represents the sum ofthe signal average power and the noise average power, and the valley ofthe autocorrelation function represents the noise average power.According to a definition of the signal-to-noise ratio, thesignal-to-noise ratio SNR can be determined based on a peak R_(auto)^(Δ)(N) and a valley R_(auto) ^(∇)(N) according to a signal-to-noiseratio determination rule, where the signal-to-noise ratio determinationrule is expressed as

${SNR} = {\frac{{{R_{auto}^{\Delta}(N)}} - {{R_{auto}^{\nabla}(N)}}}{{R_{auto}^{\nabla}(N)}}.}$

It can be seen that, with this solution, the signal-to-noise ratio canbe accurately estimated by performing a simple operation on the peak andthe valley of the autocorrelation function.

Based on the above embodiments, in this embodiment, the process ofacquiring the peak and the valley of the autocorrelation function isperformed by the following steps including:

determining an autocorrelation function R_(auto)(k+N) which is expressedas

${{R_{auto}\left( {k + N} \right)} = {\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{r\left( {k + m} \right)}{r\left( {k + m + N} \right)}^{*}}}}},$

where k represents a subscript related to time, N represents a length ofthe repetitive training sequence, r(k+m) represents a signal at a timeinstant delayed than a time instant k by m sampling periods, mrepresents the number of delayed sampling periods, and (.)* represents aconjugate operation;

determining a peak R_(auto) ^(Δ)(N) of the autocorrelation function fromthe autocorrelation function R_(auto)(k+N), where in a case that thereis no frequency offset in the information transmission system, the peakR_(auto) ^(Δ)(N) is determined as

$\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{P_{signal} + P_{noise}}}\end{matrix},$

and in a case that there is a frequency offset which is expressed asε=f_(offset)/Δf in the information transmission system, the peakR_(auto) ^(Δ)(N) is determined as

$\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{{P_{signal}e^{j\; 2\pi \; k\; ɛ\text{/}N}} + P_{noise}}}\end{matrix},$

where f_(offset) represents a carrier offset, and Δf represents asubcarrier frequency interval; and

determining a valley R_(auto) ^(∇)(N) of the autocorrelation functionfrom the autocorrelation function R_(auto)(k+N), where the valleyR_(auto) ^(∇)(N) is determined as

$\begin{matrix}{{R_{auto}^{\nabla}(N)} = {\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{w\left( {k_{\nabla} + m} \right)}\left\lbrack {{s_{preamble}\left( {k_{\nabla} + m} \right)} + {w\left( {k_{\nabla} + m + N} \right)}} \right\rbrack}^{*}}}} \\{= P_{noise}}\end{matrix},$

where s_(preamble)(k) represents a training sequence, w(k) represents anoise, P_(signal) represents a signal average power, P_(noise)represents a noise average power, k_(Δ) represents a time subscriptcorresponding to the peak, and k_(∇) represents a time subscriptcorresponding to the valley.

Reference is made to FIG. 3, which is a schematic diagram showing asignal-to-noise ratio estimation based on a structure of a repetitivetraining sequence according to an embodiment of the present disclosure.It should be noted that, there may be multiple repetitive trainingsequences in an actual system, but the multiple repetitive trainingsequences do not bring a qualitative change to the signal-to-noise ratioestimation. The valley and the peak are acquired from results of firsttwo repetitive training sequences among the repetitive trainingsequences. In this embodiment, the following description is given bytaking only two repetitive training sequences as an example. It isassumed that, a length of the repetitive training sequence is indicatedby N. In this case, an autocorrelation function of the received signalthat is delayed by N sampling instants may be calculated as follows.

$\begin{matrix}{{R_{auto}\left( {k + N} \right)} = {\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{r\left( {k + m} \right)}{r\left( {k + m + N} \right)}^{*}}}}} & (7)\end{matrix}$

where (.)* represents a conjugate operation. In a process of calculatingthe peak of the autocorrelation function, whether there is a frequencyoffset between a receiving end and a transmitting end of the informationtransmission system should be taken in consideration. In a case thatthere is no frequency offset between the receiving end and thetransmitting end of the information transmission system, the peakR_(auto) ^(Δ)(N) of the autocorrelation function may be determined as:

$\begin{matrix}\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{P_{signal} + P_{noise}}}\end{matrix} & (8)\end{matrix}$

where s_(preamble)(m) represents a training sequence, P_(signal)represents a signal average power, P_(noise) represents a noise averagepower, and k_(Δ) represents a time subscript corresponding to the peak.It can be seen that the peak appears in a hill shape, and it takes Nsampling periods from the bottom of the hill to the top of the hill.

In a case that there is a frequency offset between the receiving end andthe transmitting end of the information transmission system, it isassumed that there is a normalized frequency offset ε=f_(offset)/Δfbetween the receiving end and the transmitting end, where f_(offset)represents a carrier offset, and Δf represents a subcarrier frequencyinterval. In this case, the peak R_(auto) ^(Δ)(N) of the autocorrelationfunction may be determined as

$\begin{matrix}\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{{s_{preamble}\left( {k_{\Delta} + m} \right)}e^{j\; 2\pi \; k\; ɛ\text{/}N}} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{{P_{signal}e^{j\; 2\pi \; k\; ɛ\text{/}N}} + P_{noise}}}\end{matrix} & (9)\end{matrix}$

It can be seen that the frequency offset does not affect the algorithmfor calculating the signal-to-noise ratio, and only causes the peak ofthe autocorrelation function to have a phase related to the frequencyoffset. The phase can be used to implement estimation for the frequencyoffset.

In addition, it is assumed that the noise in the channel is a gaussianwhite noise and is independent of the transmitted signal. In this case,the valley of the autocorrelation function R_(auto)(k+N) may bedetermined as

$\begin{matrix}\begin{matrix}{{R_{auto}^{\nabla}(N)} = {\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{w\left( {k_{\nabla} + m} \right)}\left\lbrack {{s_{preamble}\left( {k_{\nabla} + m} \right)} + {w\left( {k_{\nabla} + m + N} \right)}} \right\rbrack}^{*}}}} \\{= P_{noise}}\end{matrix} & (10)\end{matrix}$

Further, according to the definition of the signal-to-noise ratio, thesignal-to-noise ratio SNR may be determined based on the peak R_(auto)^(Δ)(N), and the valley R_(auto) ^(∇)(N) as follows.

$\begin{matrix}{{SNR} = \frac{{{R_{auto}^{\Delta}(N)}} - {{R_{auto}^{\nabla}(N)}}}{{R_{auto}^{\nabla}(N)}}} & (11)\end{matrix}$

where |⋅| represents an absolute value operation.

It can be seen from the above that, in this solution, the frequencyoffset estimation and the signal-to-noise ratio estimation can beachieved by determining the peak of the delayed autocorrelation functionof the received signal, where the absolute value of the peak is used forthe signal-to-noise ratio estimation, and the phase of the peak is usedfor the frequency offset estimation. Reference is made to FIG. 4, whichis a schematic diagram showing a relationship between an actual valueand an estimated value of an SNR according to an embodiment of thepresent disclosure. A straight line indicates the estimated value, and abroken line indicates the actual value. It can be seen that, there is asubstantially linear relationship between the estimated value and theactual value, and thus the signal-to-noise ratio of the system can bestably and reliably estimated with the solution of the presentdisclosure.

It should be noted that, the MMSE equalization algorithm expressed bythe expression (6) may result in the scale of the constellation map ofthe signal after equalization changing with factors such as thesignal-to-noise ratio. FIG. 5a shows a constellation map of an outputsignal of an MMSE equalizer in a case that the signal-to-noise ratio is4 dB, and FIG. 5b shows a constellation map of the output signal of theMMSE equalizer in a case that the signal-to-noise ratio is 12 dB. It canbe seen that, the scale of the constellation map of the output signal ofthe equalizer is decreased with increase of the signal-to-noise ratio.The decreased scale of the constellation map may result in thesubsequent soft demapping module failing to work normally. Further, inthe actual implementation process, the automatic gain control (AGC) isrequired to ensure a level of the received signal of the system.However, the AGC cannot ensure that the average power of the receivedsignal is constant, but only can ensure that the average power of thereceived signal is in a certain range. In this case, the channelestimation value may change, which also results in the change of theconstellation map of the output signal of the MMSE equalizer.

Referring to FIG. 6, a channel equalization method based on an MMSEequalizer is provided according to the embodiment of the presentdisclosure, to solve the problem that the subsequent soft demappingmodule fails to work normally, which is caused by the fact that thescale of the constellation map of the signal after equalization changeswith the factors such as the signal-to-noise ratio. The channelequalization method includes the following steps S201 to S204.

In S201, a signal-to-noise ratio and a frequency domain channel impulseresponse are acquired.

Specifically, the signal-to-noise ratio is determined by performing thesignal-to-noise ratio determining method according to any one of theabove embodiments, and the specific determination process thereof isdescribed in the embodiment of the signal-to-noise ratio determiningmethod, which is not repeated herein.

In S202, an MMSE equalizer coefficient is determined based on thesignal-to-noise ratio and the frequency domain channel impulse response.

Specifically, the MMSE equalizer coefficient in this embodiment iscalculated in the same manner as that in the expression (6). That is,the MMSE equalizer coefficient C_(MMSE)(k) is expressed as:

$\begin{matrix}{{C_{MMSE}(k)} = {\frac{{H^{*}(k)}P_{signal}}{{{{H(k)}}^{2}\mspace{14mu} P_{signal}} + P_{noise}} = \frac{H^{*}(k)}{{{H(k)}}^{2} + \frac{1}{SNR}}}} & (12)\end{matrix}$

where the signal-to-noise ratio SNR in expression (12) is obtained byperforming the signal-to-noise ratio determining method described above.

In S203, a scale correction factor is determined based on an averagefrequency domain channel response, a signal average power, and a noiseaverage power.

The process of determining the scale correction factor based on theaverage frequency domain channel response, the signal average power andthe noise average power is performed by the following steps including:

determining a scale correction factor Θ based on an average frequencydomain channel response H, a signal average power P_(signal), and anoise average power P_(noise) according to a scale correction factordetermination rule which is expressed as

$\Theta = {\frac{{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}.}$

Specifically, in this embodiment, the calculating method for the MMSEequalizer coefficient expressed by the expression (6) is modified asfollows.

$\begin{matrix}\begin{matrix}{{C_{MMSE}(k)} =} & {{\frac{{H^{*}(k)}P_{signal}}{{{{H(k)}}^{2}\mspace{14mu} P_{signal}} + P_{noise}} =}} \\ & {{\frac{{H^{*}(k)}P_{signal}}{{{{H(k)}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}\frac{{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}}} \\{=} & {{\frac{{H^{*}(k)}P_{signal}}{{{{H(k)}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}\Theta}}\end{matrix} & (13) \\{{{where}\mspace{14mu} \Theta} = \frac{{{\overset{\_}{H}}^{2}\mspace{20mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}} & (14)\end{matrix}$

In S204, n equalizing process is performed on a received frequencydomain signal based on the MMSE equalizer coefficient and the scalecorrection factor, to obtain a scale-corrected frequency domain signal.

An LS equalization algorithm expressed by the following expression (15)is given to better understand why the scale correction can be achievedby using the MMSE equalizer

$\begin{matrix}{{C_{LS}(k)} = \frac{H^{*}(k)}{{{H(k)}}^{2}}} & (15)\end{matrix}$

It can be proved that, a scale of a constellation map of an outputsignal of an LS equalizer does not change with the factors such as thesignal-to-noise ratio. Based on the above, the MMSE equalizer expressedby the expression (6) is replaced by the equalizer expressed by theexpression (13). It can be seen that, compared with the LS equalizerexpressed by the expression (15), the MMSE equalizer expressed by theexpression (13) not only can have an excellent performance by taking theinfluence of the signal-to-noise ratio into consideration, but also canhave the same property as the LS equalizer that the scale of theconstellation map of the output signal does not change with thesignal-to-noise ratio by introducing the scale correction factor Θ,thereby eliminating the change of the scale of the constellation map ofthe output signal of the equalizer due to the signal-to-noise ratio andthe channel estimation value.

Reference is made to FIG. 7, which is a schematic block diagram showingan MMSE channel equalization method according to an embodiment of thepresent disclosure. Three components are shown in FIG. 7, including anMMSE equalization unit, a scale correction factor calculating unit and ascale correcting unit. The MMSE equalization unit is used to perform aprocess by using a received frequency domain signal Y, an estimatedfrequency domain channel response H, a signal power P, and a noise powerN according to the algorithm expressed by the expression (₁₂), to obtaina constellation map {tilde over (X)} of the signal after equalization.The scale correction factor calculating unit is used to perform aprocess by using the estimated frequency domain channel response H, thesignal power P, and the noise power N according to the algorithmexpressed by the expression (13), to obtain a scale correction factor Θ.The scale correcting unit is used to obtain a scale-correctedconstellation map {tilde over (X)} based on the scale correction factorΘ and the constellation map {tilde over (X)} of the signal afterequalization.

FIG. 8a shows a scale-changed constellation map in a case that thesignal-to-noise ratio is 4 dB, and FIG. 8b shows an expectedconstellation map in the case that the signal-to-noise ratio is 4 dB. Itcan be seen that, in the embodiment of the present disclosure, the scaleof the constellation map of the output signal of the equalizer no longerchanges with the factors such as the signal-to-noise ratio and theadjustment of the AGC signal by the scale correction, and the differencebetween the scale-changed constellation map and the expectedconstellation map is small.

A signal-to-noise ratio determining device according to an embodiment ofthe present disclosure is described below. The signal-to-noise ratiodetermining device described below and the signal-to-noise ratiodetermining method described above may be referred to each other.

Referring to FIG. 9, a signal-to-noise ratio determining device for areceiving end of an information transmission system is providedaccording to an embodiment of the present disclosure. Thesignal-to-noise ratio determining device is based on an informationtransmission system in which timing synchronization is achieved by usinga structure of a repetitive training sequence. The signal-to-noise ratiodetermining device includes: a first acquiring module 110, and asignal-to-noise ratio determining module 120.

The first acquiring module 110 is configured to acquire a peak and avalley of an autocorrelation function. The peak represents a sum of asignal average power and a noise average power, and the valleyrepresents the noise average power.

The signal-to-noise ratio determining module 120 is configured todetermine a signal-to-noise ratio based on the peak and the valley.

The first acquiring module 110 includes: an autocorrelation functiondetermining unit, a first peak determining unit, a second peakdetermining unit and a valley determining unit.

The autocorrelation function determining unit is configured to determinean autocorrelation function R_(auto)(k+N) which is expressed as

${{R_{auto}\left( {k + N} \right)} = {\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{r\left( {k + m} \right)}{r\left( {k + m + N} \right)}^{*}}}}},$

where k represents a subscript related to time, N represents a length ofthe repetitive training sequence, r(k+m) represents a signal at a timeinstant delayed than a time instant k by m sampling periods, mrepresents the number of delayed sampling periods, and (.)* represents aconjugate operation.

The first peak determining unit is configured to determine a peakR_(auto) ^(Δ)(N) of the autocorrelation function from theautocorrelation function R_(auto)(k+N), where in a case that there is nofrequency offset in the information transmission system, the peakR_(auto) ^(Δ)(N) is determined as

$\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{P_{signal} + P_{noise}}}\end{matrix}.$

The second peak determining unit configured to determine the peakR_(auto) ^(Δ)(N) of the autocorrelation function from theautocorrelation function R_(auto)(k+N), where in a case that there is afrequency offset which is expressed as ε=f_(offset)/Δf in theinformation transmission system, the peak R_(auto) ^(Δ)(N) is determinedas

$\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{{P_{signal}e^{j\; 2\pi \; k\; ɛ\text{/}N}} + P_{noise}}}\end{matrix},$

where f_(offset) represents a carrier offset, and Δf represents asubcarrier frequency interval.

The valley determining unit is configured to determine a valley R_(auto)^(∇)(N) of the autocorrelation function from the autocorrelationfunction R_(auto)(k+N), where the valley R_(auto) ^(∇)(N) is determinedas

$\begin{matrix}{{R_{auto}^{\nabla}(N)} = {\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{w\left( {k_{\nabla} + m} \right)}\left\lbrack {{s_{preamble}\left( {k_{\nabla} + m} \right)} + {w\left( {k_{\nabla} + m + N} \right)}} \right\rbrack}^{*}}}} \\{= P_{noise}}\end{matrix},$

where s_(preamble)(k) represents a training sequence, w(k) represents anoise, P_(signal) represents a signal average power, P_(noise)represents a noise average power, k_(Δ) represents a time subscriptcorresponding to the peak, and k_(∇) represents a time subscriptcorresponding to the valley.

The signal-to-noise ratio determining module is configured to determinea signal-to-noise ratio SNR based on the peak R_(auto) ^(Δ)(N) and thevalley R_(auto) ^(∇)(N) according to a signal-to-noise ratiodetermination rule which is expressed as

${{SNR} = \frac{{{R_{auto}^{\Delta}(N)}} - {{R_{auto}^{\nabla}(N)}}}{{R_{auto}^{\nabla}(N)}}},$

where |⋅| represents an absolute value operation.

A signal-to-noise ratio determining device is further provided accordingto an embodiment of the present disclosure, which includes: a memory anda processor. The memory is configured to store a computer program. Theprocessor is configured to implement the steps of the signal-to-noiseratio determining method described above when executing the computerprogram.

A computer readable storage medium is further provided according to anembodiment of the present disclosure. A computer program is stored onthe computer readable storage medium. The computer program is executedby a processor to implement the steps of the signal-to-noise ratiodetermining method described above.

The storage medium may include: a U-disk, a mobile hard disk, aread-only memory (ROM), a random access memory (RAM), a disk, a disc, orany medium which can store a program code.

A channel equalization device according to an embodiment of the presentdisclosure is described below. The channel equalization device describedbelow and the channel equalization method described above can bereferred to each other.

Referring to FIG. 10, a channel equalization device based on an MMSEequalizer is provided according to an embodiment of the presentdisclosure. The channel equalization device includes: a second acquiringmodule 210, an equalizer coefficient determining module 220, a scalecorrection factor determining module 230, and a signal equalizationmodule 240.

The second acquiring module 210 is configured to: acquire thesignal-to-noise ratio determined by the signal-to-noise ratiodetermining device, and acquire a frequency domain channel impulseresponse.

The equalizer coefficient determining module 220 is configured todetermine an MMSE equalizer coefficient based on the signal-to-noiseratio and the frequency domain channel impulse response.

The scale correction factor determining module 230 is configured todetermine a scale correction factor based on an average frequency domainchannel response, a signal average power and a noise average power.

The signal equalization module 240 is configured to: perform anequalizing process on a received frequency domain signal based on theMMSE equalizer coefficient and the scale correction factor, to obtain ascale-corrected frequency domain signal.

The scale correction factor determining module is configured todetermine a scale correction factor Θ based on an average frequencydomain channel response H, a signal average power P_(signal), and anoise average power P_(noise) according to a scale correction factordetermination rule which is expressed as

$\Theta = {\frac{{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}.}$

It should be noted that, the scale correction factor determining module230 in this embodiment may be understood as the scale correction factorcalculating unit in the channel equalization method, and is used todetermine the scale correction factor. The signal equalization module240 in this embodiment includes the MMSE equalization unit and the scalecorrecting unit in the channel equalization method, and is used todetermine and correct a constellation map of a signal.

A channel equalization device based on an MMSE equalizer is furtherprovided according to an embodiment of the present disclosure, whichincludes: a memory and a processor. The memory is configured to store acomputer program. The processor is configured to implement the steps ofthe channel equalization method described above when executing thecomputer program.

A computer readable storage medium is further provided according to anembodiment of the present disclosure. A computer program is stored onthe computer readable storage medium. The computer program is executedby a processor to implement the steps of the channel equalization methoddescribed above.

The storage medium may include: a U-disk, a mobile hard disk, aread-only memory (ROM), a random access memory (RAM), a disk, a disc, orany medium which can store a program code.

Embodiments in this specification are described in a progressive manner,each of the embodiments emphasizes differences from other embodiments,and the same or similar parts among the embodiments can be referred toeach other.

Based on the above description of the disclosed embodiments, thoseskilled in the art can implement or carry out the present disclosure. Itis apparent for those skilled in the art to make various modificationsto these embodiments. The general principle defined herein may beapplied to other embodiments without departing from the spirit or scopeof the present disclosure. Therefore, the present disclosure is notlimited to the embodiments illustrated herein, but should be defined bythe widest scope consistent with the principle and novel featuresdisclosed herein.

1. A signal-to-noise ratio determining method for a receiving end of aninformation transmission system, the signal-to-noise ratio determiningmethod being based on an information transmission system in which timingsynchronization is achieved by using a structure of a repetitivetraining sequence, the signal-to-noise ratio determining methodcomprising: acquiring a peak and a valley of an autocorrelationfunction, wherein the peak represents a sum of a signal average powerand a noise average power, and the valley represents the noise averagepower; and determining a signal-to-noise ratio based on the peak and thevalley.
 2. The signal-to-noise ratio determining method according toclaim 1, wherein the acquiring a peak and a valley of an autocorrelationfunction comprises: determining an autocorrelation functionR_(auto)(k+N) which is expressed as${{R_{auto}\left( {k + N} \right)} = {\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{r\left( {k + m} \right)}{r\left( {k + m + N} \right)}^{*}}}}},$wherein k represents a subscript related to time, N represents a lengthof the repetitive training sequence, r(k+m) represents a signal at atime instant delayed than a time instant k by m sampling periods, mrepresents the number of delayed sampling periods, and (.)* represents aconjugate operation; determining a peak R_(auto) ^(Δ)(N) of theautocorrelation function from the autocorrelation functionR_(auto)(k+N), wherein in a case that there is no frequency offset inthe information transmission system, the peak R_(auto) ^(Δ)(N) isdetermined as $\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{P_{signal} + P_{noise}}}\end{matrix},$ and in a case that there is a frequency offset which isexpressed as ε=f_(offset)/Δf in the information transmission system, thepeak R_(auto) ^(Δ)(N) is determined as $\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{{P_{signal}e^{j\; 2\pi \; k\; ɛ\text{/}N}} + P_{noise}}}\end{matrix},$ wherein f_(offset) represents a carrier offset, and Δfrepresents a subcarrier frequency interval; and determining a valleyR_(auto) ^(∇)(N) of the autocorrelation function from theautocorrelation function R_(auto)(k+N), wherein the valley R_(auto)^(≡)(N) is determined as $\begin{matrix}{{R_{auto}^{\nabla}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{w\left( {k_{\nabla} + m} \right)}\left\lbrack {{s_{preamble}\left( {k_{\nabla} + m} \right)} + {w\left( {k_{\nabla} + m + N} \right)}} \right\rbrack}^{*}}}} \\{=} & {P_{noise}}\end{matrix},$ wherein s_(preamble)(k) represents a training sequence,w(k) represents a noise, P_(signal) represents a signal average power,P_(noise) represents a noise average power, k_(Δ) represents a timesubscript corresponding to the peak, and k_(∇) represents a timesubscript corresponding to the valley.
 3. The signal-to-noise ratiodetermining method according to claim 2, wherein the determining asignal-to-noise ratio based on the peak and the valley comprises:determining a signal-to-noise ratio SNR based on the peak R_(auto)^(Δ)(N) and the valley R_(auto) ^(∇)(N) according to a signal-to-noiseratio determination rule which is expressed as${{SNR} = \frac{{{R_{auto}^{\Delta}(N)}} - {{R_{auto}^{\nabla}(N)}}}{{R_{auto}^{\nabla}(N)}}},$wherein |⋅| represents an absolute value operation.
 4. A channelequalization method based on a minimum mean square error (MMSE)equalizer, the channel equalization method comprising: acquiring thesignal-to-noise ratio determined by performing the signal-to-noise ratiodetermining method according to claim 1, and acquiring a frequencydomain channel impulse response; determining an MMSE equalizercoefficient based on the signal-to-noise ratio and the frequency domainchannel impulse response; determining a scale correction factor based onan average frequency domain channel response, a signal average power anda noise average power; and performing an equalizing process on areceived frequency domain signal based on the MMSE equalizer coefficientand the scale correction factor, to obtain a scale-corrected frequencydomain signal.
 5. The channel equalization method according to claim 4,wherein the determining a scale correction factor based on an averagefrequency domain channel response, a signal average power and a noiseaverage power comprises: determining a scale correction factor Θ basedon an average frequency domain channel response H, a signal averagepower P_(signal), and a noise average power P_(noise) according to ascale correction factor determination rule which is expressed as$\Theta = {\frac{{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}.}$6. A signal-to-noise ratio determining device for a receiving end of aninformation transmission system, the signal-to-noise ratio determiningdevice being based on an information transmission system in which timingsynchronization is achieved by using a structure of a repetitivetraining sequence, the signal-to-noise ratio determining devicecomprising: a first acquiring module configured to acquire a peak and avalley of an autocorrelation function, wherein the peak represents a sumof a signal average power and a noise average power, and the valleyrepresents the noise average power; and a signal-to-noise ratiodetermining module configured to determine a signal-to-noise ratio basedon the peak and the valley.
 7. The signal-to-noise ratio determiningdevice according to claim 6, wherein the first acquiring modulecomprises: an autocorrelation function determining unit configured todetermine an autocorrelation function R_(auto)(k+N) which is expressedas${{R_{auto}\left( {k + N} \right)} = {\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{r\left( {k + m} \right)}{r\left( {k + m + N} \right)}^{*}}}}},$wherein k represents a subscript related to time, N represents a lengthof the repetitive training sequence, r(k+m) represents a signal at atime instant delayed than a time instant k by m sampling periods, mrepresents the number of delayed sampling periods, and (.)* represents aconjugate operation; a first peak determining unit configured todetermine a peak R_(auto) ^(Δ)(N) of the autocorrelation function fromthe autocorrelation function R_(auto)(k+N) wherein in a case that thereis no frequency offset in the information transmission system, the peakR_(auto) ^(Δ)(N) is determined as $\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{P_{signal} + P_{noise}}}\end{matrix};$ a second peak determining unit configured to determinethe peak R_(auto) ^(Δ)(N) of the autocorrelation function from theautocorrelation function R_(auto)(k+N) wherein in a case that there is afrequency offset which is expressed as ε=f_(offset)/Δf in theinformation transmission system, the peak R_(auto) ^(Δ)(N) is determinedas $\begin{matrix}{{R_{auto}^{\Delta}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; \left\lbrack {{s_{preamble}\left( {k_{\Delta} + m} \right)} + {w\left( {k_{\Delta} + m} \right)}} \right\rbrack}}} \\ & {\left\lbrack {{s_{preamble}\left( {k_{\Delta} + m + N} \right)} + {w\left( {k_{\Delta} + m + N} \right)}} \right\rbrack^{*}} \\{=} & {{{P_{signal}e^{j\; 2\pi \; k\; ɛ\text{/}N}} + P_{noise}}}\end{matrix},$ wherein f_(offset) represents a carrier offset, and Δfrepresents a subcarrier frequency interval; and a valley determiningunit configured to determine a valley R_(auto) ^(∇)(N) of theautocorrelation function from the autocorrelation functionR_(auto)(k+N), wherein the valley R_(auto) ^(∇)(N) is determined as$\begin{matrix}{{R_{auto}^{\nabla}(N)} =} & {{\frac{1}{N}{\sum\limits_{m = 0}^{N - 1}\; {{w\left( {k_{\nabla} + m} \right)}\left\lbrack {{s_{preamble}\left( {k_{\nabla} + m} \right)} + {w\left( {k_{\nabla} + m + N} \right)}} \right\rbrack}^{*}}}} \\{=} & {P_{noise}}\end{matrix},$ wherein s_(preamble)(k) represents a training sequence,w(k) represents a noise, P_(signal) represents a signal average power,P_(noise) represents a noise average power, k_(Δ) represents a timesubscript corresponding to the peak, and k_(∇) represents a timesubscript corresponding to the valley.
 8. The signal-to-noise ratiodetermining device according to claim 7, wherein the signal-to-noiseratio determining module is configured to determine a signal-to-noiseratio SNR based on the peak R_(auto) ^(Δ)(N) and the valley R_(auto)^(∇)(N) according to a signal-to-noise ratio determination rule which isexpressed as${{SNR} = \frac{{{R_{auto}^{\Delta}(N)}} - {{R_{auto}^{\nabla}(N)}}}{{R_{auto}^{\nabla}(N)}}},$wherein |⋅| represents an absolute value operation.
 9. A channelequalization device based on a minimum mean square error (MMSE)equalizer, the channel equalization device comprising: a secondacquiring module configured to: acquire the signal-to-noise ratiodetermined by the signal-to-noise ratio determining device according toclaim 6, and acquire a frequency domain channel impulse response; anequalizer coefficient determining module configured to determine an MMSEequalizer coefficient based on the signal-to-noise ratio and thefrequency domain channel impulse response; a scale correction factordetermining module configured to determine a scale correction factorbased on an average frequency domain channel response, a signal averagepower and a noise average power; and a signal equalization moduleconfigured to: perform an equalizing process on a received frequencydomain signal based on the MMSE equalizer coefficient and the scalecorrection factor, to obtain a scale-corrected frequency domain signal.10. The channel equalization device according to claim 9, wherein thescale correction factor determining module is configured to determine ascale correction factor Θ based on an average frequency domain channelresponse H, a signal average power P_(signal), and a noise average powerP_(noise) according to a scale correction factor determination rulewhich is expressed as$\Theta = {\frac{{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}.}$11. A channel equalization method based on a minimum mean square error(MMSE) equalizer, the channel equalization method comprising: acquiringthe signal-to-noise ratio determined by performing the signal-to-noiseratio determining method according to claim 2, and acquiring a frequencydomain channel impulse response; determining an MMSE equalizercoefficient based on the signal-to-noise ratio and the frequency domainchannel impulse response; determining a scale correction factor based onan average frequency domain channel response, a signal average power anda noise average power; and performing an equalizing process on areceived frequency domain signal based on the MMSE equalizer coefficientand the scale correction factor, to obtain a scale-corrected frequencydomain signal.
 12. A channel equalization method based on a minimum meansquare error (MMSE) equalizer, the channel equalization methodcomprising: acquiring the signal-to-noise ratio determined by performingthe signal-to-noise ratio determining method according to claim 3, andacquiring a frequency domain channel impulse response; determining anMMSE equalizer coefficient based on the signal-to-noise ratio and thefrequency domain channel impulse response; determining a scalecorrection factor based on an average frequency domain channel response,a signal average power and a noise average power; and performing anequalizing process on a received frequency domain signal based on theMMSE equalizer coefficient and the scale correction factor, to obtain ascale-corrected frequency domain signal.
 13. The channel equalizationmethod according to claim 11, wherein the determining a scale correctionfactor based on an average frequency domain channel response, a signalaverage power and a noise average power comprises: determining a scalecorrection factor Θ based on an average frequency domain channelresponse H, a signal average power P_(signal), and a noise average powerP_(noise) according to a scale correction factor determination rulewhich is expressed as$\Theta = {\frac{{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}.}$14. The channel equalization method according to claim 12, wherein thedetermining a scale correction factor based on an average frequencydomain channel response, a signal average power and a noise averagepower comprises: determining a scale correction factor Θ based on anaverage frequency domain channel response H, a signal average powerP_(signal), and a noise average power P_(noise) according to a scalecorrection factor determination rule which is expressed as$\Theta = {\frac{{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}.}$15. A channel equalization device based on a minimum mean square error(MMSE) equalizer, the channel equalization device comprising: a secondacquiring module configured to: acquire the signal-to-noise ratiodetermined by the signal-to-noise ratio determining device according toclaim 7, and acquire a frequency domain channel impulse response; anequalizer coefficient determining module configured to determine an MMSEequalizer coefficient based on the signal-to-noise ratio and thefrequency domain channel impulse response; a scale correction factordetermining module configured to determine a scale correction factorbased on an average frequency domain channel response, a signal averagepower and a noise average power; and a signal equalization moduleconfigured to: perform an equalizing process on a received frequencydomain signal based on the MMSE equalizer coefficient and the scalecorrection factor, to obtain a scale-corrected frequency domain signal.16. A channel equalization device based on a minimum mean square error(MMSE) equalizer, the channel equalization device comprising: a secondacquiring module configured to: acquire the signal-to-noise ratiodetermined by the signal-to-noise ratio determining device according toclaim 8, and acquire a frequency domain channel impulse response; anequalizer coefficient determining module configured to determine an MMSEequalizer coefficient based on the signal-to-noise ratio and thefrequency domain channel impulse response; a scale correction factordetermining module configured to determine a scale correction factorbased on an average frequency domain channel response, a signal averagepower and a noise average power; and a signal equalization moduleconfigured to: perform an equalizing process on a received frequencydomain signal based on the MMSE equalizer coefficient and the scalecorrection factor, to obtain a scale-corrected frequency domain signal.17. The channel equalization device according to claim 15, wherein thescale correction factor determining module is configured to determine ascale correction factor Θ based on an average frequency domain channelresponse H, a signal average power P_(signal), and a noise average powerP_(noise) according to a scale correction factor determination rulewhich is expressed as$\Theta = {\frac{{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}.}$18. The channel equalization device according to claim 16, wherein thescale correction factor determining module is configured to determine ascale correction factor Θ based on an average frequency domain channelresponse H, a signal average power P_(signal), and a noise average powerP_(noise) according to a scale correction factor determination rulewhich is expressed as$\Theta = {\frac{{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}} + P_{noise}}{{\overset{\_}{H}}^{2}\mspace{14mu} P_{signal}}.}$